anemoi-plugins-ecmwf-inference

v0.3.0 safe
4.0
Medium Risk

Plugins for anemoi.inference as built by ECMWF

🤖 AI Analysis

Final verdict: SAFE

The package appears safe with minimal risks across various checks. The metadata risk score is slightly elevated due to missing maintainer information and low activity, but there are no indications of malicious activities or supply-chain attacks.

  • Low network and shell execution risks
  • No obfuscation or credential harvesting patterns detected
  • Elevated metadata risk due to incomplete package details
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external API interactions.
  • Shell: No shell executions detected, indicating the package likely does not perform system-level operations.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package shows some red flags such as a lack of maintainer information and a low presence in the git repository, but no clear signs of typosquatting or other malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 12 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (732 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 46 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 93 commits in ecmwf/anemoi-plugins-ecmwf
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: ecmwf.int>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with anemoi-plugins-ecmwf-inference 
Create a weather prediction mini-app using the 'anemoi-plugins-ecmwf-inference' Python package. Your app should predict weather conditions based on historical data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Here’s a step-by-step guide to building this application:

1. **Project Setup**: Initialize your Python environment and install the necessary packages including 'anemoi-plugins-ecmwf-inference'. Ensure you have the latest version of Python installed.
2. **Data Acquisition**: Use 'anemoi-plugins-ecmwf-inference' to fetch historical weather data. This includes temperature, humidity, wind speed, and precipitation over a specified time period.
3. **Data Preprocessing**: Clean and preprocess the data to prepare it for model inference. Handle any missing values and normalize the data if necessary.
4. **Model Inference**: Utilize the pre-trained models provided by 'anemoi-plugins-ecmwf-inference' to make predictions about future weather conditions based on the processed historical data.
5. **Visualization**: Implement a user-friendly interface where users can input their location and desired forecast period. Display the predicted weather conditions using charts and graphs.
6. **User Interaction**: Allow users to interact with the app by selecting different locations and viewing the corresponding weather forecasts.
7. **Error Handling**: Ensure robust error handling to manage issues such as invalid inputs or connection errors when fetching data.
8. **Documentation**: Provide comprehensive documentation explaining how to use the app, including setup instructions and API usage guidelines.

**Suggested Features**:
- Real-time weather updates for selected locations.
- Historical weather comparison feature allowing users to compare current weather conditions with past data.
- A notification system that alerts users of significant changes in weather conditions.
- Integration with social media platforms for sharing weather forecasts.

The 'anemoi-plugins-ecmwf-inference' package will be central to fetching and processing the data required for accurate weather predictions. Ensure that you leverage its capabilities effectively throughout the development process.